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GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package

BACKGROUND: Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand t...

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Autores principales: Lemoine, Gwenaëlle G., Scott-Boyer, Marie-Pier, Ambroise, Bathilde, Périn, Olivier, Droit, Arnaud
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152313/
https://www.ncbi.nlm.nih.gov/pubmed/34034647
http://dx.doi.org/10.1186/s12859-021-04179-4
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author Lemoine, Gwenaëlle G.
Scott-Boyer, Marie-Pier
Ambroise, Bathilde
Périn, Olivier
Droit, Arnaud
author_facet Lemoine, Gwenaëlle G.
Scott-Boyer, Marie-Pier
Ambroise, Bathilde
Périn, Olivier
Droit, Arnaud
author_sort Lemoine, Gwenaëlle G.
collection PubMed
description BACKGROUND: Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. RESULTS: Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. CONCLUSION: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04179-4.
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spelling pubmed-81523132021-05-26 GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package Lemoine, Gwenaëlle G. Scott-Boyer, Marie-Pier Ambroise, Bathilde Périn, Olivier Droit, Arnaud BMC Bioinformatics Research BACKGROUND: Network-based analysis of gene expression through co-expression networks can be used to investigate modular relationships occurring between genes performing different biological functions. An extended description of each of the network modules is therefore a critical step to understand the underlying processes contributing to a disease or a phenotype. Biological integration, topology study and conditions comparison (e.g. wild vs mutant) are the main methods to do so, but to date no tool combines them all into a single pipeline. RESULTS: Here we present GWENA, a new R package that integrates gene co-expression network construction and whole characterization of the detected modules through gene set enrichment, phenotypic association, hub genes detection, topological metric computation, and differential co-expression. To demonstrate its performance, we applied GWENA on two skeletal muscle datasets from young and old patients of GTEx study. Remarkably, we prioritized a gene whose involvement was unknown in the muscle development and growth. Moreover, new insights on the variations in patterns of co-expression were identified. The known phenomena of connectivity loss associated with aging was found coupled to a global reorganization of the relationships leading to expression of known aging related functions. CONCLUSION: GWENA is an R package available through Bioconductor (https://bioconductor.org/packages/release/bioc/html/GWENA.html) that has been developed to perform extended analysis of gene co-expression networks. Thanks to biological and topological information as well as differential co-expression, the package helps to dissect the role of genes relationships in diseases conditions or targeted phenotypes. GWENA goes beyond existing packages that perform co-expression analysis by including new tools to fully characterize modules, such as differential co-expression, additional enrichment databases, and network visualization. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12859-021-04179-4. BioMed Central 2021-05-25 /pmc/articles/PMC8152313/ /pubmed/34034647 http://dx.doi.org/10.1186/s12859-021-04179-4 Text en © The Author(s) 2021 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Lemoine, Gwenaëlle G.
Scott-Boyer, Marie-Pier
Ambroise, Bathilde
Périn, Olivier
Droit, Arnaud
GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package
title GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package
title_full GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package
title_fullStr GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package
title_full_unstemmed GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package
title_short GWENA: gene co-expression networks analysis and extended modules characterization in a single Bioconductor package
title_sort gwena: gene co-expression networks analysis and extended modules characterization in a single bioconductor package
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8152313/
https://www.ncbi.nlm.nih.gov/pubmed/34034647
http://dx.doi.org/10.1186/s12859-021-04179-4
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